{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data path is assumed to be 'data' under repo root\n",
    "def csv_loader(filename, datapath = \"./data\", stats = True):\n",
    "    print('Loading %s.' % filename)\n",
    "    path = os.path.join(datapath, filename)\n",
    "    try:\n",
    "        with open(path, 'rb') as f:\n",
    "              data = pd.read_csv(f)\n",
    "    except Exception as e:\n",
    "        print('Unable to load data ', path, ':', e)\n",
    "    if stats:\n",
    "        print(\"{:d} rows of data loaded.\".format(len(data)))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading application_train.csv.\n",
      "307511 rows of data loaded.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "\n",
       "[3 rows x 122 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = csv_loader(\"application_train.csv\")\n",
    "df_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307511 data, 122 columns, 16 catagorical\n"
     ]
    }
   ],
   "source": [
    "col_categorical = [col for col in df_train.columns if df_train[col].dtype == 'object']\n",
    "\n",
    "print(\"{:d} data, {:d} columns, {:d} catagorical\".format(len(df_train), len(df_train.columns), len(col_categorical)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR                               int64\n",
      "TARGET                                   int64\n",
      "NAME_CONTRACT_TYPE                       object\n",
      "CODE_GENDER                              object\n",
      "FLAG_OWN_CAR                             object\n",
      "FLAG_OWN_REALTY                          object\n",
      "CNT_CHILDREN                             int64\n",
      "AMT_INCOME_TOTAL                         float64\n",
      "AMT_CREDIT                               float64\n",
      "AMT_ANNUITY                              float64\n",
      "AMT_GOODS_PRICE                          float64\n",
      "NAME_TYPE_SUITE                          object\n",
      "NAME_INCOME_TYPE                         object\n",
      "NAME_EDUCATION_TYPE                      object\n",
      "NAME_FAMILY_STATUS                       object\n",
      "NAME_HOUSING_TYPE                        object\n",
      "REGION_POPULATION_RELATIVE               float64\n",
      "DAYS_BIRTH                               int64\n",
      "DAYS_EMPLOYED                            int64\n",
      "DAYS_REGISTRATION                        float64\n",
      "DAYS_ID_PUBLISH                          int64\n",
      "OWN_CAR_AGE                              float64\n",
      "FLAG_MOBIL                               int64\n",
      "FLAG_EMP_PHONE                           int64\n",
      "FLAG_WORK_PHONE                          int64\n",
      "FLAG_CONT_MOBILE                         int64\n",
      "FLAG_PHONE                               int64\n",
      "FLAG_EMAIL                               int64\n",
      "OCCUPATION_TYPE                          object\n",
      "CNT_FAM_MEMBERS                          float64\n",
      "REGION_RATING_CLIENT                     int64\n",
      "REGION_RATING_CLIENT_W_CITY              int64\n",
      "WEEKDAY_APPR_PROCESS_START               object\n",
      "HOUR_APPR_PROCESS_START                  int64\n",
      "REG_REGION_NOT_LIVE_REGION               int64\n",
      "REG_REGION_NOT_WORK_REGION               int64\n",
      "LIVE_REGION_NOT_WORK_REGION              int64\n",
      "REG_CITY_NOT_LIVE_CITY                   int64\n",
      "REG_CITY_NOT_WORK_CITY                   int64\n",
      "LIVE_CITY_NOT_WORK_CITY                  int64\n",
      "ORGANIZATION_TYPE                        object\n",
      "EXT_SOURCE_1                             float64\n",
      "EXT_SOURCE_2                             float64\n",
      "EXT_SOURCE_3                             float64\n",
      "APARTMENTS_AVG                           float64\n",
      "BASEMENTAREA_AVG                         float64\n",
      "YEARS_BEGINEXPLUATATION_AVG              float64\n",
      "YEARS_BUILD_AVG                          float64\n",
      "COMMONAREA_AVG                           float64\n",
      "ELEVATORS_AVG                            float64\n",
      "ENTRANCES_AVG                            float64\n",
      "FLOORSMAX_AVG                            float64\n",
      "FLOORSMIN_AVG                            float64\n",
      "LANDAREA_AVG                             float64\n",
      "LIVINGAPARTMENTS_AVG                     float64\n",
      "LIVINGAREA_AVG                           float64\n",
      "NONLIVINGAPARTMENTS_AVG                  float64\n",
      "NONLIVINGAREA_AVG                        float64\n",
      "APARTMENTS_MODE                          float64\n",
      "BASEMENTAREA_MODE                        float64\n",
      "YEARS_BEGINEXPLUATATION_MODE             float64\n",
      "YEARS_BUILD_MODE                         float64\n",
      "COMMONAREA_MODE                          float64\n",
      "ELEVATORS_MODE                           float64\n",
      "ENTRANCES_MODE                           float64\n",
      "FLOORSMAX_MODE                           float64\n",
      "FLOORSMIN_MODE                           float64\n",
      "LANDAREA_MODE                            float64\n",
      "LIVINGAPARTMENTS_MODE                    float64\n",
      "LIVINGAREA_MODE                          float64\n",
      "NONLIVINGAPARTMENTS_MODE                 float64\n",
      "NONLIVINGAREA_MODE                       float64\n",
      "APARTMENTS_MEDI                          float64\n",
      "BASEMENTAREA_MEDI                        float64\n",
      "YEARS_BEGINEXPLUATATION_MEDI             float64\n",
      "YEARS_BUILD_MEDI                         float64\n",
      "COMMONAREA_MEDI                          float64\n",
      "ELEVATORS_MEDI                           float64\n",
      "ENTRANCES_MEDI                           float64\n",
      "FLOORSMAX_MEDI                           float64\n",
      "FLOORSMIN_MEDI                           float64\n",
      "LANDAREA_MEDI                            float64\n",
      "LIVINGAPARTMENTS_MEDI                    float64\n",
      "LIVINGAREA_MEDI                          float64\n",
      "NONLIVINGAPARTMENTS_MEDI                 float64\n",
      "NONLIVINGAREA_MEDI                       float64\n",
      "FONDKAPREMONT_MODE                       object\n",
      "HOUSETYPE_MODE                           object\n",
      "TOTALAREA_MODE                           float64\n",
      "WALLSMATERIAL_MODE                       object\n",
      "EMERGENCYSTATE_MODE                      object\n",
      "OBS_30_CNT_SOCIAL_CIRCLE                 float64\n",
      "DEF_30_CNT_SOCIAL_CIRCLE                 float64\n",
      "OBS_60_CNT_SOCIAL_CIRCLE                 float64\n",
      "DEF_60_CNT_SOCIAL_CIRCLE                 float64\n",
      "DAYS_LAST_PHONE_CHANGE                   float64\n",
      "FLAG_DOCUMENT_2                          int64\n",
      "FLAG_DOCUMENT_3                          int64\n",
      "FLAG_DOCUMENT_4                          int64\n",
      "FLAG_DOCUMENT_5                          int64\n",
      "FLAG_DOCUMENT_6                          int64\n",
      "FLAG_DOCUMENT_7                          int64\n",
      "FLAG_DOCUMENT_8                          int64\n",
      "FLAG_DOCUMENT_9                          int64\n",
      "FLAG_DOCUMENT_10                         int64\n",
      "FLAG_DOCUMENT_11                         int64\n",
      "FLAG_DOCUMENT_12                         int64\n",
      "FLAG_DOCUMENT_13                         int64\n",
      "FLAG_DOCUMENT_14                         int64\n",
      "FLAG_DOCUMENT_15                         int64\n",
      "FLAG_DOCUMENT_16                         int64\n",
      "FLAG_DOCUMENT_17                         int64\n",
      "FLAG_DOCUMENT_18                         int64\n",
      "FLAG_DOCUMENT_19                         int64\n",
      "FLAG_DOCUMENT_20                         int64\n",
      "FLAG_DOCUMENT_21                         int64\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR               float64\n",
      "AMT_REQ_CREDIT_BUREAU_DAY                float64\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK               float64\n",
      "AMT_REQ_CREDIT_BUREAU_MON                float64\n",
      "AMT_REQ_CREDIT_BUREAU_QRT                float64\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR               float64\n"
     ]
    }
   ],
   "source": [
    "for col in df_train.columns:\n",
    "    print(\"{:40s}\".format(col), df_train[col].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                     0\n",
       "TARGET                         0\n",
       "CNT_CHILDREN                   0\n",
       "DAYS_BIRTH                     0\n",
       "DAYS_EMPLOYED                  0\n",
       "DAYS_ID_PUBLISH                0\n",
       "FLAG_MOBIL                     0\n",
       "FLAG_EMP_PHONE                 0\n",
       "FLAG_WORK_PHONE                0\n",
       "FLAG_CONT_MOBILE               0\n",
       "FLAG_PHONE                     0\n",
       "FLAG_EMAIL                     0\n",
       "REGION_RATING_CLIENT           0\n",
       "REGION_RATING_CLIENT_W_CITY    0\n",
       "HOUR_APPR_PROCESS_START        0\n",
       "REG_REGION_NOT_LIVE_REGION     0\n",
       "REG_REGION_NOT_WORK_REGION     0\n",
       "LIVE_REGION_NOT_WORK_REGION    0\n",
       "REG_CITY_NOT_LIVE_CITY         0\n",
       "REG_CITY_NOT_WORK_CITY         0\n",
       "LIVE_CITY_NOT_WORK_CITY        0\n",
       "FLAG_DOCUMENT_2                0\n",
       "FLAG_DOCUMENT_3                0\n",
       "FLAG_DOCUMENT_4                0\n",
       "FLAG_DOCUMENT_5                0\n",
       "FLAG_DOCUMENT_6                0\n",
       "FLAG_DOCUMENT_7                0\n",
       "FLAG_DOCUMENT_8                0\n",
       "FLAG_DOCUMENT_9                0\n",
       "FLAG_DOCUMENT_10               0\n",
       "FLAG_DOCUMENT_11               0\n",
       "FLAG_DOCUMENT_12               0\n",
       "FLAG_DOCUMENT_13               0\n",
       "FLAG_DOCUMENT_14               0\n",
       "FLAG_DOCUMENT_15               0\n",
       "FLAG_DOCUMENT_16               0\n",
       "FLAG_DOCUMENT_17               0\n",
       "FLAG_DOCUMENT_18               0\n",
       "FLAG_DOCUMENT_19               0\n",
       "FLAG_DOCUMENT_20               0\n",
       "FLAG_DOCUMENT_21               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_int = [col for col in df_train.columns if df_train[col].dtype == 'int64']\n",
    "df_train[col_int].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_EMP_PHONE</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_CONT_MOBILE</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_12</th>\n",
       "      <th>FLAG_DOCUMENT_13</th>\n",
       "      <th>FLAG_DOCUMENT_14</th>\n",
       "      <th>FLAG_DOCUMENT_15</th>\n",
       "      <th>FLAG_DOCUMENT_16</th>\n",
       "      <th>FLAG_DOCUMENT_17</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.00000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278180.518577</td>\n",
       "      <td>0.080729</td>\n",
       "      <td>0.417052</td>\n",
       "      <td>-16036.995067</td>\n",
       "      <td>63815.045904</td>\n",
       "      <td>-2994.202373</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.819889</td>\n",
       "      <td>0.199368</td>\n",
       "      <td>0.998133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>0.002936</td>\n",
       "      <td>0.00121</td>\n",
       "      <td>0.009928</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102790.175348</td>\n",
       "      <td>0.272419</td>\n",
       "      <td>0.722121</td>\n",
       "      <td>4363.988632</td>\n",
       "      <td>141275.766519</td>\n",
       "      <td>1509.450419</td>\n",
       "      <td>0.001803</td>\n",
       "      <td>0.384280</td>\n",
       "      <td>0.399526</td>\n",
       "      <td>0.043164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002550</td>\n",
       "      <td>0.059268</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>0.03476</td>\n",
       "      <td>0.099144</td>\n",
       "      <td>0.016327</td>\n",
       "      <td>0.089798</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>0.022518</td>\n",
       "      <td>0.018299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-25229.000000</td>\n",
       "      <td>-17912.000000</td>\n",
       "      <td>-7197.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189145.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>-4299.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>278202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>-3254.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>367142.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>-1720.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-7489.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_CURR         TARGET   CNT_CHILDREN     DAYS_BIRTH  \\\n",
       "count  307511.000000  307511.000000  307511.000000  307511.000000   \n",
       "mean   278180.518577       0.080729       0.417052  -16036.995067   \n",
       "std    102790.175348       0.272419       0.722121    4363.988632   \n",
       "min    100002.000000       0.000000       0.000000  -25229.000000   \n",
       "25%    189145.500000       0.000000       0.000000  -19682.000000   \n",
       "50%    278202.000000       0.000000       0.000000  -15750.000000   \n",
       "75%    367142.500000       0.000000       1.000000  -12413.000000   \n",
       "max    456255.000000       1.000000      19.000000   -7489.000000   \n",
       "\n",
       "       DAYS_EMPLOYED  DAYS_ID_PUBLISH     FLAG_MOBIL  FLAG_EMP_PHONE  \\\n",
       "count  307511.000000    307511.000000  307511.000000   307511.000000   \n",
       "mean    63815.045904     -2994.202373       0.999997        0.819889   \n",
       "std    141275.766519      1509.450419       0.001803        0.384280   \n",
       "min    -17912.000000     -7197.000000       0.000000        0.000000   \n",
       "25%     -2760.000000     -4299.000000       1.000000        1.000000   \n",
       "50%     -1213.000000     -3254.000000       1.000000        1.000000   \n",
       "75%      -289.000000     -1720.000000       1.000000        1.000000   \n",
       "max    365243.000000         0.000000       1.000000        1.000000   \n",
       "\n",
       "       FLAG_WORK_PHONE  FLAG_CONT_MOBILE        ...         FLAG_DOCUMENT_12  \\\n",
       "count    307511.000000     307511.000000        ...            307511.000000   \n",
       "mean          0.199368          0.998133        ...                 0.000007   \n",
       "std           0.399526          0.043164        ...                 0.002550   \n",
       "min           0.000000          0.000000        ...                 0.000000   \n",
       "25%           0.000000          1.000000        ...                 0.000000   \n",
       "50%           0.000000          1.000000        ...                 0.000000   \n",
       "75%           0.000000          1.000000        ...                 0.000000   \n",
       "max           1.000000          1.000000        ...                 1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_13  FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  \\\n",
       "count     307511.000000     307511.000000      307511.00000     307511.000000   \n",
       "mean           0.003525          0.002936           0.00121          0.009928   \n",
       "std            0.059268          0.054110           0.03476          0.099144   \n",
       "min            0.000000          0.000000           0.00000          0.000000   \n",
       "25%            0.000000          0.000000           0.00000          0.000000   \n",
       "50%            0.000000          0.000000           0.00000          0.000000   \n",
       "75%            0.000000          0.000000           0.00000          0.000000   \n",
       "max            1.000000          1.000000           1.00000          1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_17  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  \\\n",
       "count     307511.000000     307511.000000     307511.000000     307511.000000   \n",
       "mean           0.000267          0.008130          0.000595          0.000507   \n",
       "std            0.016327          0.089798          0.024387          0.022518   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%            0.000000          0.000000          0.000000          0.000000   \n",
       "75%            0.000000          0.000000          0.000000          0.000000   \n",
       "max            1.000000          1.000000          1.000000          1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_21  \n",
       "count     307511.000000  \n",
       "mean           0.000335  \n",
       "std            0.018299  \n",
       "min            0.000000  \n",
       "25%            0.000000  \n",
       "50%            0.000000  \n",
       "75%            0.000000  \n",
       "max            1.000000  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_int].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMT_INCOME_TOTAL                     0\n",
       "AMT_CREDIT                           0\n",
       "AMT_ANNUITY                         12\n",
       "AMT_GOODS_PRICE                    278\n",
       "REGION_POPULATION_RELATIVE           0\n",
       "DAYS_REGISTRATION                    0\n",
       "OWN_CAR_AGE                     202929\n",
       "CNT_FAM_MEMBERS                      2\n",
       "EXT_SOURCE_1                    173378\n",
       "EXT_SOURCE_2                       660\n",
       "EXT_SOURCE_3                     60965\n",
       "APARTMENTS_AVG                  156061\n",
       "BASEMENTAREA_AVG                179943\n",
       "YEARS_BEGINEXPLUATATION_AVG     150007\n",
       "YEARS_BUILD_AVG                 204488\n",
       "COMMONAREA_AVG                  214865\n",
       "ELEVATORS_AVG                   163891\n",
       "ENTRANCES_AVG                   154828\n",
       "FLOORSMAX_AVG                   153020\n",
       "FLOORSMIN_AVG                   208642\n",
       "LANDAREA_AVG                    182590\n",
       "LIVINGAPARTMENTS_AVG            210199\n",
       "LIVINGAREA_AVG                  154350\n",
       "NONLIVINGAPARTMENTS_AVG         213514\n",
       "NONLIVINGAREA_AVG               169682\n",
       "APARTMENTS_MODE                 156061\n",
       "BASEMENTAREA_MODE               179943\n",
       "YEARS_BEGINEXPLUATATION_MODE    150007\n",
       "YEARS_BUILD_MODE                204488\n",
       "COMMONAREA_MODE                 214865\n",
       "                                 ...  \n",
       "LIVINGAPARTMENTS_MODE           210199\n",
       "LIVINGAREA_MODE                 154350\n",
       "NONLIVINGAPARTMENTS_MODE        213514\n",
       "NONLIVINGAREA_MODE              169682\n",
       "APARTMENTS_MEDI                 156061\n",
       "BASEMENTAREA_MEDI               179943\n",
       "YEARS_BEGINEXPLUATATION_MEDI    150007\n",
       "YEARS_BUILD_MEDI                204488\n",
       "COMMONAREA_MEDI                 214865\n",
       "ELEVATORS_MEDI                  163891\n",
       "ENTRANCES_MEDI                  154828\n",
       "FLOORSMAX_MEDI                  153020\n",
       "FLOORSMIN_MEDI                  208642\n",
       "LANDAREA_MEDI                   182590\n",
       "LIVINGAPARTMENTS_MEDI           210199\n",
       "LIVINGAREA_MEDI                 154350\n",
       "NONLIVINGAPARTMENTS_MEDI        213514\n",
       "NONLIVINGAREA_MEDI              169682\n",
       "TOTALAREA_MODE                  148431\n",
       "OBS_30_CNT_SOCIAL_CIRCLE          1021\n",
       "DEF_30_CNT_SOCIAL_CIRCLE          1021\n",
       "OBS_60_CNT_SOCIAL_CIRCLE          1021\n",
       "DEF_60_CNT_SOCIAL_CIRCLE          1021\n",
       "DAYS_LAST_PHONE_CHANGE               1\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR       41519\n",
       "AMT_REQ_CREDIT_BUREAU_DAY        41519\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK       41519\n",
       "AMT_REQ_CREDIT_BUREAU_MON        41519\n",
       "AMT_REQ_CREDIT_BUREAU_QRT        41519\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR       41519\n",
       "Length: 65, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_num = [col for col in df_train.columns if df_train[col].dtype == 'float64']\n",
    "df_train[col_num].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>...</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>307499.000000</td>\n",
       "      <td>3.072330e+05</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>104582.000000</td>\n",
       "      <td>307509.000000</td>\n",
       "      <td>134133.000000</td>\n",
       "      <td>3.068510e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>307510.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.687979e+05</td>\n",
       "      <td>5.990260e+05</td>\n",
       "      <td>27108.573909</td>\n",
       "      <td>5.383962e+05</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>-4986.120328</td>\n",
       "      <td>12.061091</td>\n",
       "      <td>2.152665</td>\n",
       "      <td>0.502130</td>\n",
       "      <td>5.143927e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143421</td>\n",
       "      <td>1.405292</td>\n",
       "      <td>0.100049</td>\n",
       "      <td>-962.858788</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>0.267395</td>\n",
       "      <td>0.265474</td>\n",
       "      <td>1.899974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.371231e+05</td>\n",
       "      <td>4.024908e+05</td>\n",
       "      <td>14493.737315</td>\n",
       "      <td>3.694465e+05</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>3522.886321</td>\n",
       "      <td>11.944812</td>\n",
       "      <td>0.910682</td>\n",
       "      <td>0.211062</td>\n",
       "      <td>1.910602e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.446698</td>\n",
       "      <td>2.379803</td>\n",
       "      <td>0.362291</td>\n",
       "      <td>826.808487</td>\n",
       "      <td>0.083849</td>\n",
       "      <td>0.110757</td>\n",
       "      <td>0.204685</td>\n",
       "      <td>0.916002</td>\n",
       "      <td>0.794056</td>\n",
       "      <td>1.869295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1615.500000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-24672.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014568</td>\n",
       "      <td>8.173617e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4292.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-7479.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.334007</td>\n",
       "      <td>3.924574e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1570.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.471500e+05</td>\n",
       "      <td>5.135310e+05</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-4504.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>5.659614e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-757.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-2010.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.675053</td>\n",
       "      <td>6.636171e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-274.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.170000e+08</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>258025.500000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.962693</td>\n",
       "      <td>8.549997e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AMT_INCOME_TOTAL    AMT_CREDIT    AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "count      3.075110e+05  3.075110e+05  307499.000000     3.072330e+05   \n",
       "mean       1.687979e+05  5.990260e+05   27108.573909     5.383962e+05   \n",
       "std        2.371231e+05  4.024908e+05   14493.737315     3.694465e+05   \n",
       "min        2.565000e+04  4.500000e+04    1615.500000     4.050000e+04   \n",
       "25%        1.125000e+05  2.700000e+05   16524.000000     2.385000e+05   \n",
       "50%        1.471500e+05  5.135310e+05   24903.000000     4.500000e+05   \n",
       "75%        2.025000e+05  8.086500e+05   34596.000000     6.795000e+05   \n",
       "max        1.170000e+08  4.050000e+06  258025.500000     4.050000e+06   \n",
       "\n",
       "       REGION_POPULATION_RELATIVE  DAYS_REGISTRATION    OWN_CAR_AGE  \\\n",
       "count               307511.000000      307511.000000  104582.000000   \n",
       "mean                     0.020868       -4986.120328      12.061091   \n",
       "std                      0.013831        3522.886321      11.944812   \n",
       "min                      0.000290      -24672.000000       0.000000   \n",
       "25%                      0.010006       -7479.500000       5.000000   \n",
       "50%                      0.018850       -4504.000000       9.000000   \n",
       "75%                      0.028663       -2010.000000      15.000000   \n",
       "max                      0.072508           0.000000      91.000000   \n",
       "\n",
       "       CNT_FAM_MEMBERS   EXT_SOURCE_1  EXT_SOURCE_2  \\\n",
       "count    307509.000000  134133.000000  3.068510e+05   \n",
       "mean          2.152665       0.502130  5.143927e-01   \n",
       "std           0.910682       0.211062  1.910602e-01   \n",
       "min           1.000000       0.014568  8.173617e-08   \n",
       "25%           2.000000       0.334007  3.924574e-01   \n",
       "50%           2.000000       0.505998  5.659614e-01   \n",
       "75%           3.000000       0.675053  6.636171e-01   \n",
       "max          20.000000       0.962693  8.549997e-01   \n",
       "\n",
       "                  ...              DEF_30_CNT_SOCIAL_CIRCLE  \\\n",
       "count             ...                         306490.000000   \n",
       "mean              ...                              0.143421   \n",
       "std               ...                              0.446698   \n",
       "min               ...                              0.000000   \n",
       "25%               ...                              0.000000   \n",
       "50%               ...                              0.000000   \n",
       "75%               ...                              0.000000   \n",
       "max               ...                             34.000000   \n",
       "\n",
       "       OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  \\\n",
       "count             306490.000000             306490.000000   \n",
       "mean                   1.405292                  0.100049   \n",
       "std                    2.379803                  0.362291   \n",
       "min                    0.000000                  0.000000   \n",
       "25%                    0.000000                  0.000000   \n",
       "50%                    0.000000                  0.000000   \n",
       "75%                    2.000000                  0.000000   \n",
       "max                  344.000000                 24.000000   \n",
       "\n",
       "       DAYS_LAST_PHONE_CHANGE  AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "count           307510.000000               265992.000000   \n",
       "mean              -962.858788                    0.006402   \n",
       "std                826.808487                    0.083849   \n",
       "min              -4292.000000                    0.000000   \n",
       "25%              -1570.000000                    0.000000   \n",
       "50%               -757.000000                    0.000000   \n",
       "75%               -274.000000                    0.000000   \n",
       "max                  0.000000                    4.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "count              265992.000000               265992.000000   \n",
       "mean                    0.007000                    0.034362   \n",
       "std                     0.110757                    0.204685   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     9.000000                    8.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "count              265992.000000              265992.000000   \n",
       "mean                    0.267395                   0.265474   \n",
       "std                     0.916002                   0.794056   \n",
       "min                     0.000000                   0.000000   \n",
       "25%                     0.000000                   0.000000   \n",
       "50%                     0.000000                   0.000000   \n",
       "75%                     0.000000                   0.000000   \n",
       "max                    27.000000                 261.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "count               265992.000000  \n",
       "mean                     1.899974  \n",
       "std                      1.869295  \n",
       "min                      0.000000  \n",
       "25%                      0.000000  \n",
       "50%                      1.000000  \n",
       "75%                      3.000000  \n",
       "max                     25.000000  \n",
       "\n",
       "[8 rows x 65 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_num].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE                 0\n",
       "CODE_GENDER                        0\n",
       "FLAG_OWN_CAR                       0\n",
       "FLAG_OWN_REALTY                    0\n",
       "NAME_TYPE_SUITE                 1292\n",
       "NAME_INCOME_TYPE                   0\n",
       "NAME_EDUCATION_TYPE                0\n",
       "NAME_FAMILY_STATUS                 0\n",
       "NAME_HOUSING_TYPE                  0\n",
       "OCCUPATION_TYPE                96391\n",
       "WEEKDAY_APPR_PROCESS_START         0\n",
       "ORGANIZATION_TYPE                  0\n",
       "FONDKAPREMONT_MODE            210295\n",
       "HOUSETYPE_MODE                154297\n",
       "WALLSMATERIAL_MODE            156341\n",
       "EMERGENCYSTATE_MODE           145755\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_categorical].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>NAME_TYPE_SUITE</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
       "      <th>ORGANIZATION_TYPE</th>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <th>WALLSMATERIAL_MODE</th>\n",
       "      <th>EMERGENCYSTATE_MODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>306219</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>211120</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>97216</td>\n",
       "      <td>153214</td>\n",
       "      <td>151170</td>\n",
       "      <td>161756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>TUESDAY</td>\n",
       "      <td>Business Entity Type 3</td>\n",
       "      <td>reg oper account</td>\n",
       "      <td>block of flats</td>\n",
       "      <td>Panel</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>278232</td>\n",
       "      <td>202448</td>\n",
       "      <td>202924</td>\n",
       "      <td>213312</td>\n",
       "      <td>248526</td>\n",
       "      <td>158774</td>\n",
       "      <td>218391</td>\n",
       "      <td>196432</td>\n",
       "      <td>272868</td>\n",
       "      <td>55186</td>\n",
       "      <td>53901</td>\n",
       "      <td>67992</td>\n",
       "      <td>73830</td>\n",
       "      <td>150503</td>\n",
       "      <td>66040</td>\n",
       "      <td>159428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "count              307511      307511       307511          307511   \n",
       "unique                  2           3            2               2   \n",
       "top            Cash loans           F            N               Y   \n",
       "freq               278232      202448       202924          213312   \n",
       "\n",
       "       NAME_TYPE_SUITE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
       "count           306219           307511                         307511   \n",
       "unique               7                8                              5   \n",
       "top      Unaccompanied          Working  Secondary / secondary special   \n",
       "freq            248526           158774                         218391   \n",
       "\n",
       "       NAME_FAMILY_STATUS  NAME_HOUSING_TYPE OCCUPATION_TYPE  \\\n",
       "count              307511             307511          211120   \n",
       "unique                  6                  6              18   \n",
       "top               Married  House / apartment        Laborers   \n",
       "freq               196432             272868           55186   \n",
       "\n",
       "       WEEKDAY_APPR_PROCESS_START       ORGANIZATION_TYPE FONDKAPREMONT_MODE  \\\n",
       "count                      307511                  307511              97216   \n",
       "unique                          7                      58                  4   \n",
       "top                       TUESDAY  Business Entity Type 3   reg oper account   \n",
       "freq                        53901                   67992              73830   \n",
       "\n",
       "        HOUSETYPE_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  \n",
       "count           153214             151170              161756  \n",
       "unique               3                  7                   2  \n",
       "top     block of flats              Panel                  No  \n",
       "freq            150503              66040              159428  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_categorical].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Bureau Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading bureau_features.csv.\n",
      "356255 rows of data loaded.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>train_test</th>\n",
       "      <th>count_bureau</th>\n",
       "      <th>no_bureau</th>\n",
       "      <th>large_bureau_count</th>\n",
       "      <th>('DAYS_CREDIT', 'mean')</th>\n",
       "      <th>('DAYS_CREDIT', 'max')</th>\n",
       "      <th>('DAYS_CREDIT', 'min')</th>\n",
       "      <th>('DAYS_CREDIT', 'std')</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>DAYS_CREDIT_mainb</th>\n",
       "      <th>max_overdue</th>\n",
       "      <th>has_overdue</th>\n",
       "      <th>Consumer credit</th>\n",
       "      <th>Credit card</th>\n",
       "      <th>Interbank credit</th>\n",
       "      <th>Loan</th>\n",
       "      <th>Mortgage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>train</td>\n",
       "      <td>8.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-874.00</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>-1437.0</td>\n",
       "      <td>431.451040</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>train</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1400.75</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>-2586.0</td>\n",
       "      <td>909.826128</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>train</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-867.00</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>-1326.0</td>\n",
       "      <td>649.124025</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR train_test  count_bureau  no_bureau  large_bureau_count  \\\n",
       "0      100002      train           8.0      False               False   \n",
       "1      100003      train           4.0      False               False   \n",
       "2      100004      train           2.0      False               False   \n",
       "\n",
       "   ('DAYS_CREDIT', 'mean')  ('DAYS_CREDIT', 'max')  ('DAYS_CREDIT', 'min')  \\\n",
       "0                  -874.00                  -103.0                 -1437.0   \n",
       "1                 -1400.75                  -606.0                 -2586.0   \n",
       "2                  -867.00                  -408.0                 -1326.0   \n",
       "\n",
       "   ('DAYS_CREDIT', 'std')  DAYS_CREDIT  DAYS_CREDIT_mainb  max_overdue  \\\n",
       "0              431.451040       -103.0             -103.0          0.0   \n",
       "1              909.826128       -606.0             -606.0          0.0   \n",
       "2              649.124025       -408.0             -408.0          0.0   \n",
       "\n",
       "  has_overdue  Consumer credit  Credit card  Interbank credit  Loan  Mortgage  \n",
       "0       False              4.0          4.0               0.0   0.0       0.0  \n",
       "1       False              2.0          2.0               0.0   0.0       0.0  \n",
       "2       False              2.0          0.0               0.0   0.0       0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bureau = csv_loader(\"bureau_features.csv\", datapath = \"./features/\")\n",
    "df_bureau.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>('DAYS_CREDIT', 'std')</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>DAYS_CREDIT_mainb</th>\n",
       "      <th>max_overdue</th>\n",
       "      <th>has_overdue</th>\n",
       "      <th>Consumer credit</th>\n",
       "      <th>Credit card</th>\n",
       "      <th>Interbank credit</th>\n",
       "      <th>Loan</th>\n",
       "      <th>Mortgage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>431.451040</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>909.826128</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>-606.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>649.124025</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>-408.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1149.0</td>\n",
       "      <td>-1149.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "     ...     ('DAYS_CREDIT', 'std') DAYS_CREDIT DAYS_CREDIT_mainb max_overdue  \\\n",
       "0    ...                 431.451040      -103.0            -103.0         0.0   \n",
       "1    ...                 909.826128      -606.0            -606.0         0.0   \n",
       "2    ...                 649.124025      -408.0            -408.0         0.0   \n",
       "3    ...                        NaN         NaN               NaN         NaN   \n",
       "4    ...                        NaN     -1149.0           -1149.0         0.0   \n",
       "\n",
       "  has_overdue Consumer credit  Credit card  Interbank credit  Loan  Mortgage  \n",
       "0       False             4.0          4.0               0.0   0.0       0.0  \n",
       "1       False             2.0          2.0               0.0   0.0       0.0  \n",
       "2       False             2.0          0.0               0.0   0.0       0.0  \n",
       "3         NaN             NaN          NaN               NaN   NaN       NaN  \n",
       "4       False             1.0          0.0               0.0   0.0       0.0  \n",
       "\n",
       "[5 rows x 139 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.merge(df_bureau, on = \"SK_ID_CURR\", how = \"left\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR                               int64\n",
      "train_test                               object\n",
      "count_bureau                             float64\n",
      "no_bureau                                bool\n",
      "large_bureau_count                       bool\n",
      "('DAYS_CREDIT', 'mean')                  float64\n",
      "('DAYS_CREDIT', 'max')                   float64\n",
      "('DAYS_CREDIT', 'min')                   float64\n",
      "('DAYS_CREDIT', 'std')                   float64\n",
      "DAYS_CREDIT                              float64\n",
      "DAYS_CREDIT_mainb                        float64\n",
      "max_overdue                              float64\n",
      "has_overdue                              object\n",
      "Consumer credit                          float64\n",
      "Credit card                              float64\n",
      "Interbank credit                         float64\n",
      "Loan                                     float64\n",
      "Mortgage                                 float64\n"
     ]
    }
   ],
   "source": [
    "for col in df_bureau.columns:\n",
    "    print(\"{:40s}\".format(col), df_bureau[col].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NAME_CONTRACT_TYPE',\n",
       " 'CODE_GENDER',\n",
       " 'FLAG_OWN_CAR',\n",
       " 'FLAG_OWN_REALTY',\n",
       " 'NAME_TYPE_SUITE',\n",
       " 'NAME_INCOME_TYPE',\n",
       " 'NAME_EDUCATION_TYPE',\n",
       " 'NAME_FAMILY_STATUS',\n",
       " 'NAME_HOUSING_TYPE',\n",
       " 'OCCUPATION_TYPE',\n",
       " 'WEEKDAY_APPR_PROCESS_START',\n",
       " 'ORGANIZATION_TYPE',\n",
       " 'FONDKAPREMONT_MODE',\n",
       " 'HOUSETYPE_MODE',\n",
       " 'WALLSMATERIAL_MODE',\n",
       " 'EMERGENCYSTATE_MODE',\n",
       " 'train_test',\n",
       " 'no_bureau',\n",
       " 'large_bureau_count',\n",
       " 'has_overdue']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_categorical += [col for col in df_bureau.columns if df_bureau[col].dtype != \"int64\" and df_bureau[col].dtype != \"float64\"]\n",
    "col_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WALLSMATERIAL_MODE',\n",
       " 'CODE_GENDER',\n",
       " 'has_overdue',\n",
       " 'FLAG_DOCUMENT_8',\n",
       " 'FLAG_DOCUMENT_11',\n",
       " 'NAME_INCOME_TYPE',\n",
       " 'EMERGENCYSTATE_MODE',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'NAME_TYPE_SUITE',\n",
       " 'OCCUPATION_TYPE',\n",
       " 'FLAG_DOCUMENT_20',\n",
       " 'FLAG_DOCUMENT_15',\n",
       " 'FLAG_DOCUMENT_19',\n",
       " 'FLAG_DOCUMENT_12',\n",
       " 'FLAG_DOCUMENT_18',\n",
       " 'ORGANIZATION_TYPE',\n",
       " 'NAME_CONTRACT_TYPE',\n",
       " 'FLAG_OWN_REALTY',\n",
       " 'FLAG_CONT_MOBILE',\n",
       " 'FLAG_DOCUMENT_5',\n",
       " 'FLAG_DOCUMENT_16',\n",
       " 'NAME_EDUCATION_TYPE',\n",
       " 'NAME_FAMILY_STATUS',\n",
       " 'no_bureau',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'FLAG_DOCUMENT_4',\n",
       " 'train_test',\n",
       " 'FLAG_OWN_CAR',\n",
       " 'FONDKAPREMONT_MODE',\n",
       " 'FLAG_PHONE',\n",
       " 'FLAG_DOCUMENT_3',\n",
       " 'FLAG_WORK_PHONE',\n",
       " 'large_bureau_count',\n",
       " 'FLAG_DOCUMENT_13',\n",
       " 'FLAG_DOCUMENT_2',\n",
       " 'FLAG_DOCUMENT_14',\n",
       " 'FLAG_DOCUMENT_17',\n",
       " 'FLAG_EMAIL',\n",
       " 'NAME_HOUSING_TYPE',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'WEEKDAY_APPR_PROCESS_START',\n",
       " 'FLAG_MOBIL',\n",
       " 'FLAG_DOCUMENT_9',\n",
       " 'FLAG_DOCUMENT_21',\n",
       " 'HOUSETYPE_MODE',\n",
       " 'FLAG_DOCUMENT_10']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_categorical_feats = list(set(col_categorical + [col for col in df_train.columns if col[:4] == \"FLAG\"]))\n",
    "all_categorical_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>...</th>\n",
       "      <th>('DAYS_CREDIT', 'min')</th>\n",
       "      <th>('DAYS_CREDIT', 'std')</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>DAYS_CREDIT_mainb</th>\n",
       "      <th>max_overdue</th>\n",
       "      <th>Consumer credit</th>\n",
       "      <th>Credit card</th>\n",
       "      <th>Interbank credit</th>\n",
       "      <th>Loan</th>\n",
       "      <th>Mortgage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>307499.000000</td>\n",
       "      <td>3.072330e+05</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>227419.000000</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>263491.00000</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>263491.000000</td>\n",
       "      <td>263491.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278180.518577</td>\n",
       "      <td>0.080729</td>\n",
       "      <td>0.417052</td>\n",
       "      <td>1.687979e+05</td>\n",
       "      <td>5.990260e+05</td>\n",
       "      <td>27108.573909</td>\n",
       "      <td>5.383962e+05</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>-16036.995067</td>\n",
       "      <td>63815.045904</td>\n",
       "      <td>...</td>\n",
       "      <td>-1762.374882</td>\n",
       "      <td>614.802762</td>\n",
       "      <td>-489.297817</td>\n",
       "      <td>-489.297817</td>\n",
       "      <td>4.772759</td>\n",
       "      <td>4.059380</td>\n",
       "      <td>1.30499</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.137591</td>\n",
       "      <td>0.059232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102790.175348</td>\n",
       "      <td>0.272419</td>\n",
       "      <td>0.722121</td>\n",
       "      <td>2.371231e+05</td>\n",
       "      <td>4.024908e+05</td>\n",
       "      <td>14493.737315</td>\n",
       "      <td>3.694465e+05</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>4363.988632</td>\n",
       "      <td>141275.766519</td>\n",
       "      <td>...</td>\n",
       "      <td>863.862181</td>\n",
       "      <td>335.459760</td>\n",
       "      <td>537.574145</td>\n",
       "      <td>537.574145</td>\n",
       "      <td>89.141274</td>\n",
       "      <td>3.497836</td>\n",
       "      <td>1.50277</td>\n",
       "      <td>0.001948</td>\n",
       "      <td>0.613063</td>\n",
       "      <td>0.260005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1615.500000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-25229.000000</td>\n",
       "      <td>-17912.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-2922.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2922.000000</td>\n",
       "      <td>-2922.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189145.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-2583.000000</td>\n",
       "      <td>353.405534</td>\n",
       "      <td>-620.000000</td>\n",
       "      <td>-620.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>278202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.471500e+05</td>\n",
       "      <td>5.135310e+05</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1827.000000</td>\n",
       "      <td>620.569550</td>\n",
       "      <td>-300.000000</td>\n",
       "      <td>-300.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>367142.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1035.000000</td>\n",
       "      <td>849.796436</td>\n",
       "      <td>-143.000000</td>\n",
       "      <td>-143.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.170000e+08</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>258025.500000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>-7489.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2042.831491</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2792.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 119 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_CURR         TARGET   CNT_CHILDREN  AMT_INCOME_TOTAL  \\\n",
       "count  307511.000000  307511.000000  307511.000000      3.075110e+05   \n",
       "mean   278180.518577       0.080729       0.417052      1.687979e+05   \n",
       "std    102790.175348       0.272419       0.722121      2.371231e+05   \n",
       "min    100002.000000       0.000000       0.000000      2.565000e+04   \n",
       "25%    189145.500000       0.000000       0.000000      1.125000e+05   \n",
       "50%    278202.000000       0.000000       0.000000      1.471500e+05   \n",
       "75%    367142.500000       0.000000       1.000000      2.025000e+05   \n",
       "max    456255.000000       1.000000      19.000000      1.170000e+08   \n",
       "\n",
       "         AMT_CREDIT    AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "count  3.075110e+05  307499.000000     3.072330e+05   \n",
       "mean   5.990260e+05   27108.573909     5.383962e+05   \n",
       "std    4.024908e+05   14493.737315     3.694465e+05   \n",
       "min    4.500000e+04    1615.500000     4.050000e+04   \n",
       "25%    2.700000e+05   16524.000000     2.385000e+05   \n",
       "50%    5.135310e+05   24903.000000     4.500000e+05   \n",
       "75%    8.086500e+05   34596.000000     6.795000e+05   \n",
       "max    4.050000e+06  258025.500000     4.050000e+06   \n",
       "\n",
       "       REGION_POPULATION_RELATIVE     DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "count               307511.000000  307511.000000  307511.000000   \n",
       "mean                     0.020868  -16036.995067   63815.045904   \n",
       "std                      0.013831    4363.988632  141275.766519   \n",
       "min                      0.000290  -25229.000000  -17912.000000   \n",
       "25%                      0.010006  -19682.000000   -2760.000000   \n",
       "50%                      0.018850  -15750.000000   -1213.000000   \n",
       "75%                      0.028663  -12413.000000    -289.000000   \n",
       "max                      0.072508   -7489.000000  365243.000000   \n",
       "\n",
       "           ...        ('DAYS_CREDIT', 'min')  ('DAYS_CREDIT', 'std')  \\\n",
       "count      ...                 263491.000000           227419.000000   \n",
       "mean       ...                  -1762.374882              614.802762   \n",
       "std        ...                    863.862181              335.459760   \n",
       "min        ...                  -2922.000000                0.000000   \n",
       "25%        ...                  -2583.000000              353.405534   \n",
       "50%        ...                  -1827.000000              620.569550   \n",
       "75%        ...                  -1035.000000              849.796436   \n",
       "max        ...                      0.000000             2042.831491   \n",
       "\n",
       "         DAYS_CREDIT  DAYS_CREDIT_mainb    max_overdue  Consumer credit  \\\n",
       "count  263491.000000      263491.000000  263491.000000    263491.000000   \n",
       "mean     -489.297817        -489.297817       4.772759         4.059380   \n",
       "std       537.574145         537.574145      89.141274         3.497836   \n",
       "min     -2922.000000       -2922.000000       0.000000         0.000000   \n",
       "25%      -620.000000        -620.000000       0.000000         2.000000   \n",
       "50%      -300.000000        -300.000000       0.000000         3.000000   \n",
       "75%      -143.000000        -143.000000       0.000000         6.000000   \n",
       "max         0.000000           0.000000    2792.000000        86.000000   \n",
       "\n",
       "        Credit card  Interbank credit           Loan       Mortgage  \n",
       "count  263491.00000     263491.000000  263491.000000  263491.000000  \n",
       "mean        1.30499          0.000004       0.137591       0.059232  \n",
       "std         1.50277          0.001948       0.613063       0.260005  \n",
       "min         0.00000          0.000000       0.000000       0.000000  \n",
       "25%         0.00000          0.000000       0.000000       0.000000  \n",
       "50%         1.00000          0.000000       0.000000       0.000000  \n",
       "75%         2.00000          0.000000       0.000000       0.000000  \n",
       "max        22.00000          1.000000      79.000000      13.000000  \n",
       "\n",
       "[8 rows x 119 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exclude_feats = [\"train_test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, precision_recall_curve, auc, average_precision_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV, cross_val_score\n",
    "\n",
    "for col in all_categorical_feats:\n",
    "    df_train[col] = df_train[col].astype('category') \n",
    "X = df_train.drop(['TARGET', 'SK_ID_CURR'] + exclude_feats, axis=1) \n",
    "Y = df_train['TARGET']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=123, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2718026401211859, roc_auc=0.752273553610627, total=   8.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   11.6s remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.28085751407535725, roc_auc=0.7610534798197153, total=  10.4s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   26.3s remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2750242954324587, roc_auc=0.7590819797837572, total=  10.4s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   41.1s remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27492792253712667, roc_auc=0.7560586890964648, total=  11.3s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   57.3s remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2740936271076873, roc_auc=0.756298951267286, total=  10.8s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.2min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27115520138775956, roc_auc=0.7510412095368442, total=  10.3s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:  1.5min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27893110737709365, roc_auc=0.7594669151003743, total=  10.2s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  1.7min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2771357598012745, roc_auc=0.7596436218686677, total=  11.1s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  2.0min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2771470476397414, roc_auc=0.7561142138550297, total=   9.9s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  2.2min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2731419372149182, roc_auc=0.7563340418014207, total=  10.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  2.4min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2769016697588126, roc_auc=0.7446063022630891, total=  10.1s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:  2.7min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2805549185966521, roc_auc=0.7511046983984063, total=  11.5s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:  3.0min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27974995698801397, roc_auc=0.7517948210990086, total=  11.3s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:  3.2min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2757380662374572, roc_auc=0.7458854233498159, total=  10.9s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:  3.5min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2746016334456565, roc_auc=0.7493339979159875, total=  11.5s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  3.8min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27312521598894135, roc_auc=0.7462771347253012, total=  10.6s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:  4.0min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.28087001198835415, roc_auc=0.7527598952116508, total=  13.6s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:  4.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27645266969366056, roc_auc=0.749528292119566, total=  12.3s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:  4.6min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.28018709938211006, roc_auc=0.7497418919769524, total=  10.1s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:  4.9min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2770850017160508, roc_auc=0.7493983454056126, total=  10.5s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:  5.1min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2708596903427917, roc_auc=0.7342987636871765, total=  10.1s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:  5.4min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2763276870215203, roc_auc=0.7403399687710057, total=  10.1s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:  5.6min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2760059612518629, roc_auc=0.7414249681442439, total=   9.9s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:  5.8min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2757074471308247, roc_auc=0.739266705137114, total=  10.2s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:  6.1min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.273604244543591, roc_auc=0.7378242080911424, total=  10.5s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:  6.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2700126681546722, roc_auc=0.7346976981697441, total=  10.7s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:  6.6min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2804056105410164, roc_auc=0.7407100588488199, total=  10.6s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:  6.9min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2753094540453268, roc_auc=0.7419110703957106, total=  11.1s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:  7.1min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27249637155297535, roc_auc=0.7388634144821975, total=  10.3s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:  7.4min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2737715763517171, roc_auc=0.7365578316828647, total=  10.3s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  7.6min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  7.6min finished\n"
     ]
    }
   ],
   "source": [
    "estimator = lgb.LGBMClassifier(boosting_type='gbdt', class_weight='balanced', silent=False)\n",
    "param_grid = {\n",
    "    'n_estimators': [200],\n",
    "    'max_depth':[5],\n",
    "    'max_delta_step':[0,2],\n",
    "    'learning_rate': [0.1,0.2,0.3] \n",
    "}\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "gbm = GridSearchCV(estimator, param_grid,  cv=kfold,  verbose=100, scoring=['f1','roc_auc'], refit='roc_auc')\n",
    "grid_result = gbm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.756953 using {'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.756953 (0.002982) with: {'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.756520 (0.003120) with: {'learning_rate': 0.1, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.748545 (0.002840) with: {'learning_rate': 0.2, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.749541 (0.002053) with: {'learning_rate': 0.2, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.738631 (0.002472) with: {'learning_rate': 0.3, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.738548 (0.002641) with: {'learning_rate': 0.3, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1044x1944 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_roc_auc']\n",
    "stds = grid_result.cv_results_['std_test_roc_auc']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "        print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "\n",
    "lgb.plot_importance(grid_result.best_estimator_)\n",
    "plt.gcf().set_size_inches(14.5, 27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": 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iSJKkUtkmA2yTSZKkwpkMSZJUKpMhwGRIkiQVzmRIkqRSedNFwGRIkiQVzmRIkqRSuWYIsBiSJKlYaTEE2CaTJEmFMxmSJKlUJkOAyZAkSSqcyZAkSaXy2WSAxZAkSeWyTQbYJpMkSYUzGZIkqVQmQ4DJkCRJKpzJkCRJhco0GQKLIUmSymWbDLBNJkmSCmcyJElSqUyGAJMhSZJUOJMhSZIK5VPrKyZDkiSpaCZDkiSVymQIsBiSJKlcPqcVsE0mSZIKZzIkSVKhXEBdMRmSJElFMxmSJKlUJkOAxZAkSeVyATVgm0ySJBXOZEiSpEK5gLpiMiRJkopmMiRJUqlcMwRYDEmSVCzbZBXbZJIkqWgmQ5Iklco2GWAyJEmSCmcyJElSodJkCLAYkiSpXBZDgG0ySZJUOJMhSZIKZZusYjIkSZKKZjIkSVKpTIYAkyFJklQ4kyFJkgrlmqGKxZAkSYWyGKrYJpMkSUUzGZIkqVAmQxWTIUmSVDSTIUmSSpXR7Bn0ChZDkiQVyjZZxTaZJEkqmsmQJEmFylbbZGAyJEmSCmcyJElSoVwzVLEYkiSpUOnVZIBtMkmSVDiTIUmSCmWbrGIyJEmSimYyJElSoby0vmIyJEmSimYyJElSoTKbPYPewWRIkqRCZWt0+7YoEbFORFwXEfdFxJSI+MpC+78eERkRq9bvIyJOj4hpEXF3RGzd5thDI+KBeju0zfg2ETG5/szpEdHpxCyGJElST5oHfC0zNwF2AI6MiE2hKpSAPYBH2xy/NzC03kYAZ9XHrgKcAGwPbAecEBEr1585qz72tc/t1dmELIYkSSpUM5KhzHwyM++oX78A3AcMrnefBhwDtG3g7QOcm5VxwKCIWBPYE7gmM2dk5kzgGmCvet9KmXlrZiZwLrBvZ3OyGJIkSd0mIkZExMQ224hOjh0CbAXcFhEfBh7PzEkLHTYYeKzN++n1WGfj09sZ75ALqCVJKlQjFlBn5khg5KKOi4gVgD8AR1G1zo4Hhrd3aHuneRPjHTIZkiSpUM1okwFERF+qQuiCzLwM2ABYD5gUEf8E1gbuiIi3UyU767T5+NrAE4sYX7ud8Q5ZDEmSpB5TX9l1NnBfZv4UIDMnZ+bqmTkkM4dQFTRbZ+a/gDHAIfVVZTsAszLzSWAsMDwiVq4XTg8Hxtb7XoiIHepzHQJc3tmcOmyTRcRKnX0wM5/v4u+WJEm9UJOeWv9u4GBgckTcVY99KzOv6uD4q4D3A9OAucBhAJk5IyK+B0yoj/tuZs6oXx8BnAMsC/yl3jrU2ZqhKbyx9/ba+wTW7eyLJUmSFpaZN9P+up62xwxp8zqBIzs4bhQwqp3xicDmXZ1Th8VQZq7T0T5JkrTk86n1lS5dTRYRBwLrZ+b3I2JtYI3MvL2xU5MkSY3U2pw2Wa+zyAXUEfEL4H1U/T2o+nW/bOSkJEmSekpXkqEdM3PriLgTFixY6tfgeUmSpAZr0gLqXqcrl9a/GhHLUN+wKCLeBthllCRJS4WuJENnUN0YabWIOBH4OHBiQ2clSZIarqs3SVzaLbIYysxzI+J2YPd66GOZeU9jpyVJktQzuvpsshbgVapWmXetliRpKdCIZ5MtibpyNdnxwIXAWlTP9/h9RBzX6IlJkqTGatazyXqbriRDnwS2ycy5ABFxMnA78INGTkySJKkndKUYemSh4/oADzVmOpIkqad408VKZw9qPY1qjdBcYEpEjK3fDwdu7pnpSZIkNVZnydBrV4xNAa5sMz6ucdORJEk9xZsuVjp7UOvZPTkRSZLUs7yarLLINUMRsQFwMrApMOC18cwc1sB5SZIk9YiuLKA+BzgJ+DGwN3AYPo5DkqQlnguoK125geJymTkWIDMfzMxvUz3FXpIkaYnXlWTo5YgI4MGI+DzwOLB6Y6clSZIazQXUla4UQ18FVgC+TLV2aCDw6UZOSpIkNZ4LqCtdeVDrbfXLF4CDGzsdSZKkntXZTRf/SHWTxXZl5n4NmZEkSeoRLqCudJYM/aLHZtGOZdfauZmnl4q1fL8Biz5IUkMc2+wJFKqzmy7+vScnIkmSepYLqCtdubRekiRpqdWVq8kkSdJSyDVDlS4XQxHRPzNfbuRkJElSz/HK+soi22QRsV1ETAYeqN9vERE/b/jMJEmSekBXkqHTgQ8CfwLIzEkR4eM4JElawtkmq3RlAfUymfnIQmPzGzEZSZKkntaVZOixiNgOyIhoAb4E3N/YaUmSpEbz0vpKV4qhI6haZesCTwF/q8ckSdISrLXZE+gluvJssqeBA3tgLpIkST1ukcVQRPyadq6+y8wRDZmRJEnqEYltMuham+xvbV4PAD4CPNaY6UiSJPWsrrTJLmr7PiLOA65p2IwkSVKPaPWui8CbexzHesA7unsikiSpZ7XaJgO6tmZoJv9ZM7QMMAM4tpGTkiRJ6imdFkMREcAWwOP1UGtmGqpJkrQUcAF1pdM7UNeFzx8zc369WQhJkqSlSlcexzE+IrZu+EwkSVKPam3AtiTqsE0WEX0ycx6wE/DZiHgQmAMEVWhkgSRJkpZ4na0ZGg9sDezbQ3ORJEk9yDVDlc6KoQDIzAd7aC6SJKkHLaltre7WWTG0WkQc3dHOzPxpA+YjSZLUozorhlqAFcAMTZKkpZHJUKWzYujJzPxuj81EkiSpCRa5ZkiSJC2dXEBd6awY2q3HZiFJknpcq7UQ0MlNFzNzRk9ORJIkqRnezFPrJUnSUsCn1le68jgOSZKkpZbJkCRJhfLp6xWLIUmSCuV9hiq2ySRJUtFMhiRJKlRruIAaTIYkSVLhTIYkSSqUC6grJkOSJKloJkOSJBXKq8kqFkOSJBXKZ5NVbJNJkqSimQxJklQon01WMRmSJElFMxmSJKlQXlpfsRiSJKlQLqCu2CaTJElFMxmSJKlQ3meoYjIkSZKKZjIkSVKhXEBdsRiSJKlQLqCu2CaTJElFMxmSJKlQLqCumAxJkqSimQxJklQok6GKyZAkSSqayZAkSYVKryYDLIYkSSqWbbKKbTJJklQ0kyFJkgplMlQxGZIkST0mIkZFxNMRcc9C41+KiKkRMSUifthm/LiImFbv27PN+F712LSIOLbN+HoRcVtEPBARF0VEv0XNyWJIkqRCZQO2LjgH2KvtQES8D9gHeGdmbgb8uB7fFDgQ2Kz+zJkR0RIRLcAZwN7ApsBB9bEApwKnZeZQYCZw+KImZDEkSVKhWqP7t0XJzBuBGQsNHwGckpkv18c8XY/vA4zOzJcz82FgGrBdvU3LzIcy8xVgNLBPRASwK3Bp/fnfAfsuak4WQ5IkqdtExIiImNhmG9GFjw0Ddq7bWzdExLb1+GDgsTbHTa/HOhp/G/BcZs5baLxTLqCWJKlQjVhAnZkjgZGL+bE+wMrADsC2wMURsT7QXtaUtB/mZCfHL/LkkiRJzTQduCwzExgfEa3AqvX4Om2OWxt4on7d3vizwKCI6FOnQ22P75BtMkmSCtXagO1N+hPVWh8iYhjQj6qwGQMcGBH9I2I9YCgwHpgADK2vHOtHtch6TF1MXQfsX3/vocDlizq5yZAkSYXq4tVf3SoiLgR2AVaNiOnACcAoYFR9uf0rwKF1YTMlIi4G7gXmAUdm5vz6e74IjAVagFGZOaU+xTeB0RFxEnAncPYi51Sdq/fp029w75yYtJRbvt+AZk9BKtas2Q/26NPCfrzuJ7v9b+3XHz1/iXvimcmQJEmF6sql8CVwzZAkSSqayZAkSYXy2WQVkyFJklQ0kyFJkgrllUoViyFJkgrVajkE2CaTJEmFMxmSJKlQLqCumAxJkqSimQxJklQoVwxVLIYkSSqUbbKKbTJJklQ0kyFJkgrls8kqJkOSJKloJkOSJBXKmy5WLIYkSSqUpVDFNpkkSSqayZAkSYXy0vqKyZAkSSqayZAkSYVyAXXFYkiSpEJZClVsk0mSpKKZDEmSVCgXUFdMhiRJUtFMhiRJKpQLqCsmQ5IkqWgmQ5IkFcpcqGIxJElSoVxAXbFNJkmSimYyJElSodJGGWAyJEmSCmcyJElSoVwzVLEYkiSpUN5nqGKbTJIkFc1kSJKkQpkLVUyGJElS0UyGJEkqlGuGKhZDkiQVyqvJKrbJ1CW/HvkTnpg+ibvu/PuCsd9fcBYTJ/yViRP+yrT7xzFxwl8BOOigjywYnzjhr7zy0mNsscVmLLvsAMb86VzumXwDk+66lu+ffFyzfo60xOjfvx/XXn8ZN996BeMm/IXjjv8KAJ/93MHcOelaZs1+kFXetvKC4wcNWonzLzyLf4y7kmuvv4xNNh3W6fdIgsjsnRFZn36De+fECrXzTtsze/Ycfvvb/2HLrXZ7w/4fnfodZj3/PCed/LPXjW+++cZcdukohm28I8suO4Dtt9ua62+4hb59+3LN2Is45dSfc/XY63rqZ6gLlu83oNlT0EKWX3455syZS58+fRh7zUV885jv8crLr/Dcc7O44i+/Z5f37MuMf88E4HsnHcvsOXM49Qc/Z+iw9fnJT0/kwx88uMPvmTjhrmb+NC1k1uwHoyfP95kh+3f739rf/PPSHv0N3cFkSF1y0823MWPmcx3u33//DzH6osvfMH7gAfty0cXV+IsvvsT1N9wCwKuvvsodd05m8OA1GzNhaSkyZ85cAPr27UPfvn3ITO6++14effTxNxy70cYbcsP11f+zB+5/iHXXHcxqq7+tw++R1IRiKCIO6+lzqrF23ml7nnr6GaZNe/gN+z62/4cYfdGf3jA+cOBKfPADe3DtdTf3xBSlJdoyyyzDTbf8mWkPj+e6a//B7RMndXjsPZPv40Mf3hOArbd5J+usO5jBa6252N+jMrQ2YFsSNSMZOrGjHRExIiImRsTE1tY5PTknvQUHHLAvF7WTCm237VbMffFFpkyZ+rrxlpYWLjjvDH5xxigefvjRnpqmtMRqbW1l5x0/xKYbvZut37XFgnVA7Tntp79i0KCB3HTLn/nc5w/h7kn3Mm/evMX+HqkkDbmaLCLu7mgXsEZHn8vMkcBIcM3QkqKlpYWP7Ls32+2w9xv2HfDxfdotkn551g95YNrDnP7z3/TEFKWlxqxZL3DzTePYfff3cN+997d7zAsvzObII7654P3dU27gkUemL/b3qAw+tb7SqGRoDeAQ4EPtbP9u0DnVBLvvtjNTp07j8ceffN14RPDRj35wwXqh13z3xGMYOHBFjv7aCT05TWmJ9bZVV2HgwBUBGDCgP7u8793cf/+DHR4/cOCK9O3bF4BDP3UAt/xjAi+8MHuxv0dlsE1WaVQxdAWwQmY+stD2T+D6Bp1TDXT+eWdw841j2GjYBvzzoYkc9qkDAfj4x/dpd+H0e3begccff/J1bbDBg9fkW8d9hU02GcaE8WOZOOGvfPqwg3rsN0hLorevsRpXXHUB/xh3Jdfd+Ceuu/Zmxl59HZ874lDunXozgwe/nVvGXcnPf/F9AIZttCG3TbiaCXf8lT2Gv5djj/lup98jyUvrJS3ES+ul5unpS+sPfsd+3f639rxHLvPSekmSpCWJj+OQJKlQtmAqFkOSJBXKB7VWbJNJkqSimQxJklQo7zNUMRmSJElFMxmSJKlQS+pNErubxZAkSYVyAXXFNpkkSSqayZAkSYVyAXXFZEiSJBXNZEiSpEK5gLpiMiRJkopmMiRJUqEyXTMEFkOSJBXLS+srtskkSVLRTIYkSSqUC6grJkOSJKloJkOSJBXKmy5WLIYkSSqUC6grtskkSVLRTIYkSSqU9xmqmAxJkqSimQxJklQoL62vWAxJklQoryar2CaTJElFMxmSJKlQXlpfMRmSJElFMxmSJKlQXlpfMRmSJElFMxmSJKlQrhmqWAxJklQoL62v2CaTJEk9KiK+GhFTIuKeiLgwIgZExHoRcVtEPBARF0VEv/rY/vX7afX+IW2+57h6fGpE7Plm52MxJElSoVozu31blIgYDHwZeFdmbg60AAcCpwKnZeZQYCZweP2Rw4GZmbkhcFp9HBGxaf25zYC9gDMjouXN/DtYDEmSpJ7WB1g2IvoAywFPArsCl9b7fwfsW7/ep35PvX+3iIh6fHRmvpyZDwPTgO3ezGQshiRJKlQ2YIuIERExsc024nVhiPaBAAAHn0lEQVTnzHwc+DHwKFURNAu4HXguM+fVh00HBtevBwOP1Z+dVx//trbj7XxmsbiAWpKkQjXiarLMHAmM7Gh/RKxMleqsBzwHXALs3d5XvfaRDvZ1NL7YTIYkSVJP2h14ODOfycxXgcuAHYFBddsMYG3gifr1dGAdgHr/QGBG2/F2PrNYLIYkSSpUK9ntWxc8CuwQEcvVa392A+4FrgP2r485FLi8fj2mfk+9/9qsbp09BjiwvtpsPWAoMP7N/DvYJpMkST0mM2+LiEuBO4B5wJ1UbbUrgdERcVI9dnb9kbOB8yJiGlUidGD9PVMi4mKqQmoecGRmzn8zc4re+lySPv0G986JSUu55fsNaPYUpGLNmv1ge+tgGmaHtXbp9r+14564vkd/Q3cwGZIkqVA+jqPimiFJklQ0kyFJkgrls8kqJkOSJKloJkOSJBWqt15E1dNMhiRJUtFMhiRJKpRXk1UshiRJKpRtsoptMkmSVDSTIUmSCmWbrGIyJEmSimYyJElSobzpYsViSJKkQrW6gBqwTSZJkgpnMiRJUqFsk1VMhiRJUtFMhiRJKpRrhioWQ5IkFco2WcU2mSRJKprJkCRJhbJNVjEZkiRJRTMZkiSpUK4ZqpgMSZKkopkMSZJUKNcMVSyGJEkqlG2yim0ySZJUNJMhSZIKldna7Cn0CiZDkiSpaCZDkiQVqtU1Q4DFkCRJxUqvJgNsk0mSpMKZDEmSVCjbZBWTIUmSVDSTIUmSCuWaoYrFkCRJhfJxHBXbZJIkqWgmQ5IkFcpnk1VMhiRJUtFMhiRJKpQLqCsmQ5IkqWgmQ5IkFcqbLlYshiRJKpRtsoptMkmSVDSTIUmSCuVNFysmQ5IkqWgmQ5IkFco1QxWLIUmSCuXVZBXbZJIkqWgmQ5IkFco2WcVkSJIkFc1kSJKkQnlpfcViSJKkQqULqAHbZJIkqXAmQ5IkFco2WcVkSJIkFc1kSJKkQnlpfcVkSJIkFc1kSJKkQnk1WcViSJKkQtkmq9gmkyRJRTMZkiSpUCZDFZMhSZJUNJMhSZIKZS5UCSMyNUJEjMjMkc2eh1Qa/+9Ji882mRplRLMnIBXK/3vSYrIYkiRJRbMYkiRJRbMYUqO4ZkFqDv/vSYvJBdSSJKloJkOSJKloFkOSJKloFkPqVhGxV0RMjYhpEXFss+cjlSIiRkXE0xFxT7PnIi1pLIbUbSKiBTgD2BvYFDgoIjZt7qykYpwD7NXsSUhLIoshdaftgGmZ+VBmvgKMBvZp8pykImTmjcCMZs9DWhJZDKk7DQYea/N+ej0mSVKvZTGk7hTtjHnvBklSr2YxpO40HVinzfu1gSeaNBdJkrrEYkjdaQIwNCLWi4h+wIHAmCbPSZKkTlkMqdtk5jzgi8BY4D7g4syc0txZSWWIiAuBW4GNImJ6RBze7DlJSwofxyFJkopmMiRJkopmMSRJkopmMSRJkopmMSRJkopmMSRJkopmMSQ1WUTMj4i7IuKeiLgkIpZ7C9+1S0RcUb/+cEQc28mxgyLiC2/iHP8vIr7e1fGFjjknIvZfjHMN8SnskhrNYkhqvhczc8vM3Bx4Bfh8251RWez/q5k5JjNP6eSQQcBiF0OStLSxGJJ6l5uADetE5L6IOBO4A1gnIoZHxK0RcUedIK0AEBF7RcT/RsTNwH6vfVFEfCoiflG/XiMi/hgRk+ptR+AUYIM6lfpRfdw3ImJCRNwdESe2+a7jI2JqRPwN2GhRPyIiPlt/z6SI+MNCadfuEXFTRNwfER+sj2+JiB+1Offn3uo/pCR1lcWQ1EtERB9gb2ByPbQRcG5mbgXMAb4N7J6ZWwMTgaMjYgDwa+BDwM7A2zv4+tOBGzJzC2BrYApwLPBgnUp9IyKGA0OB7YAtgW0i4j0RsQ3Vo1W2oiq2tu3Cz7ksM7etz3cf0PZuyEOA9wIfAH5Z/4bDgVmZuW39/Z+NiPW6cB5Jesv6NHsCklg2Iu6qX98EnA2sBTySmePq8R2ATYF/RARAP6pHL2wMPJyZDwBExPnAiHbOsStwCEBmzgdmRcTKCx0zvN7urN+vQFUcrQj8MTPn1ufoyvPmNo+Ik6hacStQPaLlNRdnZivwQEQ8VP+G4cA726wnGlif+/4unEuS3hKLIan5XszMLdsO1AXPnLZDwDWZedBCx20JdNczdQL4QWb+aqFzHPUmznEOsG9mToqITwG7tNm38Hdlfe4vZWbboomIGLKY55WkxWabTFoyjAPeHREbAkTEchExDPhfYL2I2KA+7qAOPv934Ij6sy0RsRLwAlXq85qxwKfbrEUaHBGrAzcCH4mIZSNiRaqW3KKsCDwZEX2BTyy072MRsUw95/WBqfW5j6iPJyKGRcTyXTiPJL1lJkPSEiAzn6kTlgsjon89/O3MvD8iRgBXRsSzwM3A5u18xVeAkfWTzOcDR2TmrRHxj/rS9b/U64Y2AW6tk6nZwCcz846IuAi4C3iEqpW3KP8N3FYfP5nXF11TgRuANYDPZ+ZLEfEbqrVEd0R18meAfbv2ryNJb41PrZckSUWzTSZJkopmMSRJkopmMSRJkopmMSRJkopmMSRJkopmMSRJkopmMSRJkor2/wGTps6W/OF+yAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "Y_pred = grid_result.predict(X_test)\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "\n",
    "df_cm = pd.DataFrame(confusion_matrix(Y_test, Y_pred))\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(df_cm, annot=True, fmt=\"d\", ax=ax)\n",
    "ax.set_ylabel('True label');\n",
    "ax.set_xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_xgb = pd.DataFrame(list(grid_result.predict_proba(X_test)))\n",
    "fpr_xgb, tpr_xgb, thresholds_xgb = roc_curve(Y_test, prob_xgb.iloc[:,1])\n",
    "roc_auc_xgb = auc(fpr_xgb, tpr_xgb)\n",
    "p_xgb,r_xgb,thre_xgb = precision_recall_curve(Y_test,prob_xgb.iloc[:,1])\n",
    "average_p_xgb = average_precision_score(Y_test, prob_xgb.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1.set_xlim([-0.05,1.05])\n",
    "ax1.set_ylim([-0.05,1.05])\n",
    "ax1.set_xlabel('False Positive Rate')\n",
    "ax1.set_ylabel('True Positive Rate')\n",
    "ax1.set_title('ROC Curve')\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.set_xlim([-0.05,1.05])\n",
    "ax2.set_ylim([-0.05,1.05])\n",
    "ax2.set_xlabel('Recall')\n",
    "ax2.set_ylabel('Precision')\n",
    "ax2.set_title('PR Curve')\n",
    "\n",
    "ax1.plot(fpr_xgb, tpr_xgb, lw=1, label='XGBoost: area = %0.2f'%roc_auc_xgb)\n",
    "\n",
    "ax2.plot(r_xgb, p_xgb, lw=1, label='XGBoost: area = %0.2f'%average_p_xgb)\n",
    "\n",
    "ax1.legend(loc='lower right')    \n",
    "ax2.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate accuracy for xgboost\n",
    "score_xgb = grid_result.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Binary Accuracy</th>\n",
       "      <th>AUC of ROC</th>\n",
       "      <th>AUC of PR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.754714</td>\n",
       "      <td>0.754714</td>\n",
       "      <td>0.237239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model  Binary Accuracy  AUC of ROC  AUC of PR\n",
       "0  XGBoost         0.754714    0.754714   0.237239"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_lr = 0\n",
    "roc_auc_lr = 0\n",
    "average_p_lr = 0\n",
    "\n",
    "result = {'Model':['XGBoost'],\n",
    "          'Binary Accuracy':[score_xgb],\n",
    "          'AUC of ROC':[roc_auc_xgb],\n",
    "          'AUC of PR':[average_p_xgb]}\n",
    "pd.DataFrame(result,columns=['Model', 'Binary Accuracy', 'AUC of ROC', 'AUC of PR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading application_test.csv.\n",
      "48744 rows of data loaded.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>('DAYS_CREDIT', 'std')</th>\n",
       "      <th>DAYS_CREDIT</th>\n",
       "      <th>DAYS_CREDIT_mainb</th>\n",
       "      <th>max_overdue</th>\n",
       "      <th>has_overdue</th>\n",
       "      <th>Consumer credit</th>\n",
       "      <th>Credit card</th>\n",
       "      <th>Interbank credit</th>\n",
       "      <th>Loan</th>\n",
       "      <th>Mortgage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>20560.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>489.942514</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>222768.0</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>162.297053</td>\n",
       "      <td>-62.0</td>\n",
       "      <td>-62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>663264.0</td>\n",
       "      <td>69777.0</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>393.964888</td>\n",
       "      <td>-1210.0</td>\n",
       "      <td>-1210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 138 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "0      100001         Cash loans           F            N               Y   \n",
       "1      100005         Cash loans           M            N               Y   \n",
       "2      100013         Cash loans           M            Y               Y   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          135000.0    568800.0      20560.5         450000.0   \n",
       "1             0           99000.0    222768.0      17370.0         180000.0   \n",
       "2             0          202500.0    663264.0      69777.0         630000.0   \n",
       "\n",
       "     ...    ('DAYS_CREDIT', 'std') DAYS_CREDIT DAYS_CREDIT_mainb max_overdue  \\\n",
       "0    ...                489.942514       -49.0             -49.0         0.0   \n",
       "1    ...                162.297053       -62.0             -62.0         0.0   \n",
       "2    ...                393.964888     -1210.0           -1210.0         0.0   \n",
       "\n",
       "  has_overdue  Consumer credit  Credit card  Interbank credit  Loan  Mortgage  \n",
       "0       False              7.0          0.0               0.0   0.0       0.0  \n",
       "1       False              2.0          1.0               0.0   0.0       0.0  \n",
       "2       False              2.0          0.0               0.0   2.0       0.0  \n",
       "\n",
       "[3 rows x 138 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = csv_loader(\"application_test.csv\")\n",
    "df_test = df_test.merge(df_bureau, on = \"SK_ID_CURR\", how = \"left\")\n",
    "df_test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in all_categorical_feats:\n",
    "    df_test[col] = df_test[col].astype('category') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Refit\n",
    "best_param = {'learning_rate': 0.1, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
    "est = lgb.LGBMClassifier(boosting_type='gbdt', class_weight='balanced', silent=False, **best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = est.fit(X, Y)\n",
    "prediction = pd.DataFrame(list(final_model.predict_proba(df_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test[\"TARGET\"] = prediction.iloc[:,1]\n",
    "df_test[[\"SK_ID_CURR\",\"TARGET\"]].to_csv(\"./results/lgb_model_tva.csv\", index = False)"
   ]
  }
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